Abstract
Optimal Input Design (OID) methodologies are developed to find a signal that could best estimate a set of parameters of a given model. Their application in constrained nonlinear systems, especially when the search space limits or the initial conditions are unknown, may present several difficulties due to the numerical instability related to the optimization processes. A good choice over the parameters possible ranges is a trade-off among numerical stability, search space size, and effectiveness, and it is hardly found. To deal with this problem, this paper proposes a series of changes in the Sub-Optimal Excitation Signal Generation and Optimal Parameter Estimation (SOESGOPE) methodology. First, the limits over the parameters are tightly adjusted according to their confidence. A recursive approach runs the optimization methodology, analyzes the solution's feasibility and marginal costs given by the Lagrange Multipliers, and selects a direction that could improve the system's response. This approach improves the convergence and the assertiveness of the estimation process. To validate this approach, some cases, including a parameters estimation of a mobile robot nonlinear system, are tested.

Abstract
There has been an increasing attention to the study of stress. Particularly, college students often experience high levels of stress that are linked to several negative outcomes concerning academic functioning, physical, and mental health. In this paper, we introduce the EuStress Solution, that aims to create an Information System to monitor and assess, continuously and in real-time, the stress levels of the students in order to predict burnout. The Information System will use a measuring instrument based on wearable device and machine learning techniques to collect and process stress-related data from the students without their explicit interaction. In the present study, we focus on heart rate and heart rate variability indices, by comparing baseline and stress condition. We performed different statistical tests in order to develop a complex and intelligent model. Results showed the neural network had the better model fit.

Abstract

Abstract
The way plastics are currently produced, used and disposed does not capture the economic benefits of amore 'circular' approach and is dramatically harming the environment. It is relevant to determine which European countries can be considered more or less efficient in the end-of-life of plastic products processes, what the sources of the inefficiencies are, and howthose less efficient countries could improve their performance towards a more circular economy. Although some countries have developed a variety of quantitative indicators, there is scarcity of adequate metrics for performance measurements. This paper estimates the efficiency of 26 European countries in the context of Circular Economy, for the period 2006-2016, considering the generation of waste, recovery and recycling of plastic, with a methodology based on theMultidirectional Efficiency Analysis. Apart from identifying the most efficient countries in the studied period, results show that efficiency increases for most countries with time, and that many countries reach the full efficiency by the end of the study period, and especially by 2016. Input analysis shows that increasing capital seems to be a main driver towards efficiency, since the other inputs are used with a similar efficiency by most countries. Output analysis suggest that the difference among countries efficiency is not in their reduction of total waste or emissions, but rather in the improvement of their economic growth in a circular way, that is, improving GDP but also the recovering and recycling activities. These results could be useful to design policies towards a more efficient and circular use of plastics.

Abstract
As we write this editorial, people around the world are apprehensive about their future; some are at home; some are thinking about the loved ones they cannot visit; some, unfortunately, are dying. We watch the graphs and listen to the daily news of new coronavirus cases, but be it just one or one thousand, for the those close of the ones affected, the impact is catastrophic. (...)

Abstract
Literature on analysis of inter-organizational networks mentions the benefits that collaboration networks can provide to firms, in terms of managerial decision-making, although rarely analysed in terms of their overall performance. This paper aims to identify the existence of common factors of evolutionary patterns in the networks that determine its performance and evolution through a Multiple Factor Analysis (MFA). Subsequently, a hierarchical clustering procedure was performed on the factors that determine these networks, trying to find similarities in the evolutionary behavior. Data were collected on twelve real collaboration networks, characterized by four variables: Operational Result, Stock of Knowledge, Operational Costs and Technological Distance. The hierarchical clustering allowed the identification and distinction of the networks with the worst and best performances, as well as the variables that characterize them, allowing to recognize poorly defined strategies in the constitution of some networks. © Springer Nature Switzerland AG 2020.